Voltage hysteresis during lithiation/delithiation of graphite associated with meta-stable carbon stackings

Mercer, Michael; Peng, Chao; Soares, Cindy; Hoster, Harry E.; Kramer, Denis

doi:10.1039/d0ta10403e

Cited by 47 publications

(102 citation statements)

References 74 publications

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“…Due to an oversized anode and presence of silicon, 50 % lithiation of the anode coincides with approximately 60 % SoC in the full cell. At this SoC value, the anode overpotential drops rapidly by about 30 mV and remains constant during the Stage I‐Stage II two phase equilibrium [32] . The kinetics of consumption of lithium inventory from the anode into ongoing SEI formation includes the thermodynamic equilibrium of dissolved Li + and reduced Li in graphite.…”

Section: Resultsmentioning

confidence: 98%

“…From an electrochemical perspective, the entire region for SoC>60 %, marked by the existence of (growing) LiC 6 domains (‘stage I’), [32,38] is equivalent to a fully charged cell as far as the anode is concerned. At the same SoC threshold, which coincides with the central graphite peak in the DVA curve (4 in Figure 8b), we also observed a sudden drop in the temperature dependence of the capacity fade rate (Figure 4), another indicator of a different degradation regime.…”

Section: Discussionmentioning

confidence: 99%

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High‐Energy Nickel‐Cobalt‐Aluminium Oxide (NCA) Cells on Idle: Anode‐ versus Cathode‐Driven Side Reactions

Zulke

Keil³

et al. 2021

Batteries & Supercaps

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We report on the first year of calendar ageing of commercial high‐energy 21700 lithium‐ion cells, varying over eight state of charge (SoC) and three temperature values. Lithium‐nickel‐cobalt‐aluminium oxide (NCA) and graphite with silicon suboxide (Gr‐SiOx) form cathodes and anodes of those cells, respectively. Degradation is fastest for cells at 70–80 % SoC according to monthly electrochemical check‐up tests. Cells kept at 100 % SoC do not show the fastest capacity fade but develop internal short circuits for temperatures T≥40 °C. Degradation is slowest for cells stored close to 0 % SoC at all temperatures. Rates of capacity fade and their temperature dependencies are distinctly different for SoC values below and above 60 %, respectively. Differential voltage analyses, apparent activation energy analysis, and endpoint slippage tracking provide useful insights into the degradation mechanisms and the respective roles of anode and cathode potential. We discuss how reversible losses of lithium might play a role in alleviating the rate of irreversible losses on commercial cells.

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Section: Resultsmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

High‐Energy Nickel‐Cobalt‐Aluminium Oxide (NCA) Cells on Idle: Anode‐ versus Cathode‐Driven Side Reactions

Zulke

Keil³

et al. 2021

Batteries & Supercaps

Self Cite

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“…We note in passing that potential refinements to the GPrep-DFTB parametrization are possible with little effort, by simply retraining the repulsion potential with additional training data and/or finely tuned hyperparameters. As a perspective, we intend to use this parametrization to train a cluster expansion similarly to [ 10 , 49 ], in order to perform free energy sampling and calculate the OCV curve. If that agrees with the measurements, then the non-perfect energetics of single ideal geometries is only a minor setback.…”

Section: Results and Discussionmentioning

confidence: 99%

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

et al. 2021

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Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x; x>0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

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“…Then the main two phase transitions take place in range "d" and "f". In range "d" there is a mismatch between lithiation (charge) and delithiation (discharge): stage IIL appears just during delithiation, and stage III converts directly in stage II during lithiation, as observed by several authors [33][34][35][36][37]. This asymmetry may be explained with a thermodynamic consideration: a liquid phase is favoured during deintercalation because the ions are removed randomly from the intercalate layers, requiring a minimum structural reorganization.…”

Section: Crystal Structure Of Active Materialsmentioning

confidence: 86%

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

Clerici¹,

Mocera²,

Somà³

2021

Energies

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The characterization of thickness change during operation of LFP/Graphite prismatic batteries is presented in this work. In this regard, current rate dependence, hysteresis behaviour between charge and discharge and correlation with phase changes are deepened. Experimental tests are carried out with a battery testing equipment correlated with optical laser sensors to evaluate swelling. Furthermore, thickness change is computed analytically with a mathematical model based on lattice parameters of the crystal structures of active materials. The results of the model are validated with experimental data. Thickness change is able to capture variations of the internal structure of the battery, referred to as phase change, characteristic of a certain state of charge. Furthermore, phase change shift is a characteristic of battery ageing. Being able to capture these properties with sensors mounted on the external surface the cell is a key feature for improving state of charge and state of health estimation in battery management system.

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Voltage hysteresis during lithiation/delithiation of graphite associated with meta-stable carbon stackings

Abstract: Cell voltage is a fundamental quantity used to monitor and control Li-ion batteries. The open circuit voltage (OCV) is of particular interest as it is believed to be a thermodynamic...

Cited by 47 publications

References 74 publications

High‐Energy Nickel‐Cobalt‐Aluminium Oxide (NCA) Cells on Idle: Anode‐ versus Cathode‐Driven Side Reactions

High‐Energy Nickel‐Cobalt‐Aluminium Oxide (NCA) Cells on Idle: Anode‐ versus Cathode‐Driven Side Reactions

Accessing Structural, Electronic, Transport and Mesoscale Properties of Li-GICs via a Complete DFTB Model with Machine-Learned Repulsion Potential

Experimental Characterization of Lithium-Ion Cell Strain Using Laser Sensors

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